Exploring an Ensemble-Based Approach to Atmospheric Climate Modeling and Testing at Scale
Abstract
A strict throughput requirement has placed a cap on the degree to which we can depend on the execution of single, long, fine spatial grid simulations to explore global atmospheric climate behavior. Alternatively, running an ensemble of short simulations is computationally more efficient. We test the null hypothesis that the climate statistics of a full-complexity atmospheric model derived from an ensemble of independent short simulation is equivalent to that from an equilibrated long simulation. The climate of short simulation ensembles is statistically distinguishable from that of a long simulation in terms of the distribution of global annual means, largely due to the presence of low-frequency atmospheric intrinsic variability in the long simulation. We also find that model climate statistics of the simulation ensemble are sensitive to the choice of compiler optimizations. While some answer-changing optimization choices do not effect the climate state in terms of mean, variability and extremes, aggressive optimizations can result in significantly different climate states.
- Authors:
-
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Earth Sciences and Climate Change Science Inst.
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility and Climate Change Science Inst.
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1567444
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Procedia Computer Science
- Additional Journal Information:
- Journal Volume: 108; Journal Issue: C; Journal ID: ISSN 1877-0509
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Computer Science; reproducibility; climate simulation; ensemble testing
Citation Formats
Mahajan, Salil, Gaddis, Abigail L., Evans, Katherine J., and Norman, Matthew R. Exploring an Ensemble-Based Approach to Atmospheric Climate Modeling and Testing at Scale. United States: N. p., 2017.
Web. doi:10.1016/j.procs.2017.05.259.
Mahajan, Salil, Gaddis, Abigail L., Evans, Katherine J., & Norman, Matthew R. Exploring an Ensemble-Based Approach to Atmospheric Climate Modeling and Testing at Scale. United States. https://doi.org/10.1016/j.procs.2017.05.259
Mahajan, Salil, Gaddis, Abigail L., Evans, Katherine J., and Norman, Matthew R. Fri .
"Exploring an Ensemble-Based Approach to Atmospheric Climate Modeling and Testing at Scale". United States. https://doi.org/10.1016/j.procs.2017.05.259. https://www.osti.gov/servlets/purl/1567444.
@article{osti_1567444,
title = {Exploring an Ensemble-Based Approach to Atmospheric Climate Modeling and Testing at Scale},
author = {Mahajan, Salil and Gaddis, Abigail L. and Evans, Katherine J. and Norman, Matthew R.},
abstractNote = {A strict throughput requirement has placed a cap on the degree to which we can depend on the execution of single, long, fine spatial grid simulations to explore global atmospheric climate behavior. Alternatively, running an ensemble of short simulations is computationally more efficient. We test the null hypothesis that the climate statistics of a full-complexity atmospheric model derived from an ensemble of independent short simulation is equivalent to that from an equilibrated long simulation. The climate of short simulation ensembles is statistically distinguishable from that of a long simulation in terms of the distribution of global annual means, largely due to the presence of low-frequency atmospheric intrinsic variability in the long simulation. We also find that model climate statistics of the simulation ensemble are sensitive to the choice of compiler optimizations. While some answer-changing optimization choices do not effect the climate state in terms of mean, variability and extremes, aggressive optimizations can result in significantly different climate states.},
doi = {10.1016/j.procs.2017.05.259},
journal = {Procedia Computer Science},
number = C,
volume = 108,
place = {United States},
year = {Fri Jun 09 00:00:00 EDT 2017},
month = {Fri Jun 09 00:00:00 EDT 2017}
}
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Works referencing / citing this record:
A Multivariate Approach to Ensure Statistical Reproducibility of Climate Model Simulations
conference, June 2019
- Mahajan, Salil; Evans, Katherine J.; Kennedy, Joseph H.
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LIVVkit 2.1: automated and extensible ice sheet model validation
journal, January 2019
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